WassimTenachi/PhySO

Quick Overview — What is this tool?

Physical Symbolic Optimization

This is the project's own description from GitHub — we show it as-is rather than inventing details.

What Can You Do With It?

• Download and run the complete source code on your own computer or server.
• Study how it works — every line of code is public.
• Use it in your own projects, subject to its license terms (see cost section below).
• Its GitHub topics describe its main areas: deep-learningequation-discoverymachine-learningphysicspythonreinforcement-learningsymbolic-regression

Who Is This For?

This project is listed under Physics Simulations in Science & Engineering, a category made for students, researchers, engineers and curious minds.

Do I need coding knowledge? Coding Knowledge Recommended. (Estimate based on project type: A developer tool/library — setup usually follows the README with basic coding skills. Always confirm from the repo's README.)

Is It Free?

The source code is released under the MIT license. Open-source licenses like this usually allow free use, including commercial use — but always read the actual license file in the repository before relying on it. Note: the code being free does not mean hosted services, APIs or infrastructure around it are free.

Is It Actively Maintained?

Activity level: Maintained · Last code update: 21 Feb 2026 · Created: 27 Jan 2023 · Open issues: 13

How to Get Started

1. Open the repository on GitHub and read its README — that is the official, verified setup guide.
2. To download the full code, generate this repository's public link below and run the clone command in a terminal:

Official links: Official Website ↗ · Releases ↗ · Documentation (README) ↗

We never publish installation steps we have not verified — the README above is the source of truth.

Repository Statistics

⭐ 1981 stars · 🍴 263 forks · 🐛 13 open issues · 💻 Language: Python · 📄 License: MIT · Default branch: main